Single-cell genomics and regulatory networks for 388 human brains
Abstract Single-cell genomics is a powerful tool for studying heterogeneous tissues such as the brain. Yet, little is understood about how genetic variants influence cell-level gene expression. Addressing this, we uniformly processed single-nuclei, multi-omics datasets into a resource comprising >2.8M nuclei from the prefrontal cortex across 388 individuals. For 28 cell types, we assessed population-level variation in expression and chromatin across gene families and drug targets. We identified >550K cell-type-specific regulatory elements and >1.4M single-cell expression-quantitative-trait loci, which we used to build cell-type regulatory and cell-to-cell communication networks. These networks manifest cellular changes in aging and neuropsychiatric disorders. We further constructed an integrative model accurately imputing single-cell expression and simulating perturbations; the model prioritized ∼250 disease-risk genes and drug targets with associated cell types.Summary Figure <jats:fig id="ufig1" position="float" orientation="portrait" fig-type="figure"><jats:graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="585576v2_ufig1" position="float" orientation="portrait" /></jats:fig>.
| Medienart: |
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Preprint| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
bioRxiv.org - (2024) vom: 03. Apr. Zur Gesamtaufnahme - year:2024 |
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| Sprache: |
Englisch |
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| Beteiligte Personen: |
Emani, Prashant S. [VerfasserIn] |
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| Links: |
Volltext [kostenfrei] |
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| Themen: |
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| doi: |
10.1101/2024.03.18.585576 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
XBI04297402X |
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| 245 | 1 | 0 | |a Single-cell genomics and regulatory networks for 388 human brains |
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| 520 | |a Abstract Single-cell genomics is a powerful tool for studying heterogeneous tissues such as the brain. Yet, little is understood about how genetic variants influence cell-level gene expression. Addressing this, we uniformly processed single-nuclei, multi-omics datasets into a resource comprising >2.8M nuclei from the prefrontal cortex across 388 individuals. For 28 cell types, we assessed population-level variation in expression and chromatin across gene families and drug targets. We identified >550K cell-type-specific regulatory elements and >1.4M single-cell expression-quantitative-trait loci, which we used to build cell-type regulatory and cell-to-cell communication networks. These networks manifest cellular changes in aging and neuropsychiatric disorders. We further constructed an integrative model accurately imputing single-cell expression and simulating perturbations; the model prioritized ∼250 disease-risk genes and drug targets with associated cell types.Summary Figure <jats:fig id="ufig1" position="float" orientation="portrait" fig-type="figure"><jats:graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="585576v2_ufig1" position="float" orientation="portrait" /></jats:fig> | ||
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